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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 6/2016

01.06.2016

Increasing prediction accuracy in collaborative filtering with initialized factor matrices

verfasst von: Mahdi Nasiri, Behrouz Minaei

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2016

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Abstract

Recommender systems are useful tools to give personalized recommendations to users. One of the most popular techniques used in these systems is collaborative filtering. Recommender system algorithms get into trouble with data sparsity and scalability. These challenges cause lack of convergence in our algorithms. In this research, we propose a new method based on matrix factorization which alleviates data sparsity. We suggest a new method which can be performed as a preprocessing method for initial latent factor matrices of users and items. Initialized latent factors in matrix factorization lead to two advantages: (1) sparsity and scalability would be covered and (2) convergence of algorithms would be faster. We have shown that our method has improved the accuracy of optimization-based matrix factorization technique. Also it has increased the speed of matrix factorization convergence.
Vorheriger Artikel Parallelized mining of domain knowledge on GPGPU and Xeon Phi clusters
Nächster Artikel Cellular Automata as the basis of effective and realistic agent-based models of crowd behavior

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Metadaten
Titel
Increasing prediction accuracy in collaborative filtering with initialized factor matrices
verfasst von
Mahdi Nasiri
Behrouz Minaei
Publikationsdatum
01.06.2016
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 6/2016
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-016-1717-8

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